High throughput single cell linear displacement adhesion assay

高通量单细胞线性位移粘附测定

• 批准号: 10483237
• 负责人: Lili C Kudo
• 金额: $ 35万
• 依托单位: NEUROINDX INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10483237
• 关键词: Address; Adhesions; Adhesives; Adoption; Algorithmic Analysis; Algorithms; Atomic Force Microscopy; Binding; Biochemical Process; Biological Assay; Cancer cell line; Cell Adhesion; Cell Differentiation process; Cell Line; Cell Survival; Cell physiology; Cells; Cellular Assay; Cellular biology; Chronic Kidney Failure; Complex; Computer software; Data; Development; Disease; Drug Targeting; Environment; Extracellular Matrix; Functional disorder; Gene Proteins; Goals; Health; Immune response; Individual; Laboratories; Lead; Malignant Neoplasms; Measurement; Measures; Mechanics; Metabolism; Methodology; Methods; Microfluidics; Microscope; Neoplasm Metastasis; Pathologic; Performance; Phase; Phenotype; Physiological; Population; Process; Property; Protocols documentation; Receptor Cell; Role; Surface; System; Techniques; Technology; Test Result; Testing; Time; TimeLine; Traction Force Microscopy; Translational Research; analytical tool; cell behavior; cost; cost efficient; diagnostic biomarker; dynamic system; experimental study; extracellular; improved; instrument; laser tweezer; machine learning algorithm; mechanotransduction; migration; novel; novel diagnostics; novel strategies; programs; protein expression; prototype; receptor; response; single cell analysis; stem cell fate; tool; tumor progression; user-friendly

ABSTRACT Cell adhesion is an essential process for any living cell. It is critical for cell differentiation, division, migration and specialization. Dysfunction of cell adhesion is a hallmark of various pathological phenotypes, including chronic kidney diseases, cancer, and many others. In the effort to discover new disease treatments and improve our basic understanding of single cell properties there is a strong demand for methods permitting rapid and accurate single cell adhesion measurements. Unfortunately, current technologies are lacking since they only measure adhesion in entire cell populations (e.g., microfluidics, spin disks) rather than individual cells, are too complex or costly (e.g., atomic force microscopy, optical tweezers) for widespread adoption, require sophisticated functionalized surfaces (traction force microscopy), or probe only targeted receptors (cell adhesive force microscopy) that precludes measuring total cell adhesion potential. Therefore, there is a great need for an automated, high-throughput and cost-efficient platform capable of simultaneously measuring single cell adhesion for a large population of cells. Here, we propose to develop a novel methodological approach utilizing simple disposable microfluidics cassettes (MiCs), oscillation driven cell shifts, and a conversion of each individual cell track to adhesion force via machine learning algorithms. Our initial studies provide strong support for the feasibility of the approach. This one-year Phase I project will result in a fully functional instrument through the development and integration of its critical components, which include a programmable cell shift actuator (BioShake), disposable MiCs, cell adhesion analysis algorithms, and software (SA1). In addition, the entire workflow and proof of principle experiments will be performed using multiple cell lines with various adhesive properties (SA2). We anticipate that the fully developed mature product will provide a high impact tool to promote mechanobiology studies on the key role of cell adhesion in health and disease, including such pathological conditions, such as cancer, thus facilitating further fundamental studies in cell biology and translational research.

抽象的 细胞粘附是任何活细胞的重要过程。它对于细胞分化、分裂、迁移至关重要 和专业化。细胞粘附功能障碍是各种病理表型的标志，包括 慢性肾脏疾病、癌症等。在努力发现新的疾病治疗方法和 提高我们对单细胞特性的基本了解，强烈需要能够快速 和准确的单细胞粘附测量。不幸的是，目前的技术还很缺乏，因为它们 仅测量整个细胞群（例如微流体、旋转盘）而不是单个细胞的粘附 过于复杂或昂贵（例如原子力显微镜、光镊）而无法广泛采用，需要 复杂的功能化表面（牵引力显微镜），或仅探测目标受体（细胞 粘附力显微镜）妨碍测量总细胞粘附潜力。因此，有一个伟大的 需要一个能够同时测量单个数据的自动化、高通量和经济高效的平台 大量细胞的细胞粘附。在这里，我们建议开发一种新颖的方法论 利用简单的一次性微流体盒 (MiC)、振荡驱动的细胞移位以及 每个单独的细胞通过机器学习算法追踪粘附力。我们的初步研究提供了强有力的 支持该方法的可行性。这个为期一年的第一阶段项目将建成一个功能齐全的 仪器通过其关键组件的开发和集成，其中包括可编程 细胞移位执行器 (BioShake)、一次性 MiC、细胞粘附分析算法和软件 (SA1)。在 此外，整个工作流程和原理实验证明将使用多个细胞系进行 各种粘合性能（SA2）。我们预计，完全开发的成熟产品将提供高 促进细胞粘附在健康和疾病中关键作用的机械生物学研究的影响工具， 包括癌症等病理状况，从而促进细胞的进一步基础研究 生物学和转化研究。